The Data-Driven Future of International Economic Law
Bibliographic record
Abstract
The availability of more data and new ways of analyzing it is changing the way we do empirical legal research. With the help of modern technology we can study adjudicators, awards and agreements in greater numbers, less time and more detail opening the doors for new research questions, theory building and legal technology applications for scholars and practitioners. This introduction to the Journal of International Economic Law Special Issue on new frontiers in empirical legal research provides a first take on this data-driven future. It distinguishes data-driven research from more traditional methods by pointing to (1) its “data first” attitude, (2) its ambition to look at all the available data rather than subsamples thereof and (3) its focus on computing rather than reading or counting. Data-driven research comes with new promises, but also challenges and limitations. While it allows researchers to uncover latent structures, debunk past myths and even forecast the future, it also requires new skills and competencies including an ability to tell patterns from noise in inductive data analysis. We argue that the time is ripe to overcome these challenges and to seize the opportunities of the new data-driven frontier in empirical legal scholarship.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.090 | 0.221 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.004 | 0.026 |
| Scholarly communication | 0.026 | 0.045 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.006 | 0.018 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".